{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8760f178",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from lib.authenticate import bearer\n",
    "import pandas as pd\n",
    "from lib.epoch_time import epoch_to_datetime, date_to_epoch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tulipy as ti\n",
    "import urllib.parse\n",
    "import sqlite3\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26324a31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Market Data API\n",
    "market_url = \"https://api.schwabapi.com/marketdata/v1/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5dd790fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Symbol formatting\n",
    "def format_symbol (symbol_input):\n",
    "    symbol = symbol_input.strip().upper()\n",
    "    if symbol.startswith(\"/\"):\n",
    "        return urllib.parse.quote(symbol)\n",
    "    return symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31487b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def fetch_store_plot(symbol, start_date_str, end_date_str, plot=True):\n",
    "#     \"\"\"\n",
    "#     Fetches historical market data for the given symbol and date range\n",
    "#     by making multiple API calls (max 10 days each),\n",
    "#     stores the concatenated data in a SQLite database,\n",
    "#     and optionally plots the closing prices.\n",
    "\n",
    "#     Parameters:\n",
    "#     - symbol (str): ticker or futures symbol.\n",
    "#     - start_date_str (str): start date in 'YYYYMMDD' format.\n",
    "#     - end_date_str (str): end date in 'YYYYMMDD' format.\n",
    "#     - plot (bool): whether to plot the data after fetching.\n",
    "\n",
    "#     Returns:\n",
    "#     - pd.DataFrame: full fetched price data.\n",
    "#     \"\"\"\n",
    "#     start_date = datetime.strptime(start_date_str, \"%Y%m%d\")\n",
    "#     end_date = datetime.strptime(end_date_str, \"%Y%m%d\")\n",
    "\n",
    "#     # Function to generate date ranges (10 days max each)\n",
    "#     def generate_date_ranges(start_date, end_date, step_days=10):\n",
    "#         from datetime import timedelta\n",
    "#         ranges = []\n",
    "#         current_start = start_date\n",
    "#         while current_start < end_date:\n",
    "#             current_end = min(current_start + timedelta(days=step_days), end_date)\n",
    "#             ranges.append((current_start, current_end))\n",
    "#             current_start = current_end\n",
    "#         return ranges\n",
    "\n",
    "#     all_data = pd.DataFrame()\n",
    "\n",
    "#     for start, end in generate_date_ranges(start_date, end_date):\n",
    "#         print(f\"Fetching data from {start.date()} to {end.date()} for {symbol}...\")\n",
    "#         chunk = get_price_history(symbol, start, end)  # make sure get_price_history is defined/imported\n",
    "#         if chunk is not None:\n",
    "#             all_data = pd.concat([all_data, chunk], ignore_index=True)\n",
    "\n",
    "#     # Clean duplicates and reset index\n",
    "#     all_data.drop_duplicates(subset=['datetime'], inplace=True)\n",
    "#     all_data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "#     # Save to SQLite\n",
    "#     conn = sqlite3.connect(\"schwab_data.db\")\n",
    "#     all_data.to_sql(\"price_data\", conn, if_exists=\"replace\", index=False)\n",
    "#     conn.close()\n",
    "\n",
    "#     print(\"Data fetched and stored to 'schwab_data.db' successfully.\")\n",
    "\n",
    "#     if plot and not all_data.empty:\n",
    "#         fig, ax = plt.subplots(figsize=(12, 6))\n",
    "#         ax.plot(all_data.index, all_data['close'])\n",
    "#         all_data['date'] = all_data['datetime'].dt.date\n",
    "#         unique_dates = all_data['date'].drop_duplicates()\n",
    "#         first_indices = all_data.groupby('date').head(1).index\n",
    "\n",
    "#         ax.set_xticks(first_indices)\n",
    "#         ax.set_xticklabels(unique_dates, rotation=45)\n",
    "#         plt.grid(True)\n",
    "#         plt.tight_layout()\n",
    "#         plt.title(f\"Closing Prices for {symbol.upper()}\")\n",
    "#         plt.show()\n",
    "\n",
    "#     return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e0cc21a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_date_ranges(start_date, end_date, step_days=10):\n",
    "    from datetime import timedelta\n",
    "    ranges = []\n",
    "    current_start = start_date\n",
    "    while current_start < end_date:\n",
    "        current_end = min(current_start + timedelta(days=step_days), end_date)\n",
    "        ranges.append((current_start, current_end))\n",
    "        current_start = current_end\n",
    "    return ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0289e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_and_store_all_data(symbol, start_date, end_date):\n",
    "    all_data = pd.DataFrame()\n",
    "    for start, end in generate_date_ranges(start_date, end_date):\n",
    "        print(f\"Fetching data from {start.date()} to {end.date()}...\")\n",
    "        chunk = get_price_history(symbol, start, end)\n",
    "        if chunk is not None:\n",
    "            all_data = pd.concat([all_data, chunk], ignore_index=True)\n",
    "    # Remove duplicates if any\n",
    "    all_data.drop_duplicates(subset=['datetime'], inplace=True)\n",
    "    all_data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "    # Save to SQLite\n",
    "    conn = sqlite3.connect(\"schwab_data.db\")\n",
    "    all_data.to_sql(\"price_data\", conn, if_exists=\"replace\", index=False)\n",
    "    conn.close()\n",
    "    print(\"Data saved to database.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a90b720",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_price_history(symbol, start_date, end_date, period_type=\"day\", period=None, freq_type=\"minute\", frequency=1, extended_hours=\"false\"):\n",
    "    endpoint = \"pricehistory\"\n",
    "\n",
    "    start_epoch = int(start_date.timestamp()*1000)\n",
    "    end_epoch = int(end_date.timestamp()*1000)\n",
    "    symbol = format_symbol(symbol)\n",
    "\n",
    "    if period is None:\n",
    "        # set a default period based on period_type if needed\n",
    "        period = 10\n",
    "\n",
    "    params = {\n",
    "        \"symbol\": symbol,\n",
    "        \"periodType\": period_type,\n",
    "        \"period\": period,\n",
    "        \"frequencyType\": freq_type,\n",
    "        \"frequency\": frequency,\n",
    "        \"startDate\": start_epoch,\n",
    "        \"endDate\": end_epoch,\n",
    "        \"needExtendedHoursData\": extended_hours,\n",
    "        \"needPreviousClose\": \"true\"\n",
    "    }\n",
    "\n",
    "    response = requests.get(f\"{market_url}{endpoint}\", params=params,\n",
    "        headers={f\"Authorization\": f\"Bearer {bearer}\"})\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        returned_data = response.json()\n",
    "        if 'candles' in returned_data:\n",
    "            for candle in returned_data['candles']:\n",
    "                candle['datetime'] = epoch_to_datetime(candle['datetime'])\n",
    "            return pd.DataFrame(returned_data['candles'])\n",
    "        else:\n",
    "            print(\"No candle data found.\")\n",
    "    else:\n",
    "        print(f\"Failed with status: {response.status_code}\")\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0db345e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    symbol = input(\"Symbol: \")\n",
    "    start_date = datetime.strptime(input(\"Start date (YYYYMMDD): \"), \"%Y%m%d\")\n",
    "    end_date = datetime.strptime(input(\"End date (YYYYMMDD): \"), \"%Y%m%d\")\n",
    "\n",
    "    # Example: fetch all data and store in DB\n",
    "    fetch_and_store_all_data(symbol, start_date, end_date)\n",
    "\n",
    "    # Optionally, load from DB and plot\n",
    "    conn = sqlite3.connect(\"schwab_data.db\")\n",
    "    price_df = pd.read_sql(\"SELECT * FROM price_data ORDER BY datetime\", conn, parse_dates=['datetime'])\n",
    "    conn.close()\n",
    "\n",
    "    if price_df is not None and not price_df.empty:\n",
    "        fig, ax = plt.subplots(figsize=(12, 6))\n",
    "        ax.plot(price_df.index, price_df['close'])\n",
    "        price_df['date'] = price_df['datetime'].dt.date\n",
    "        unique_dates = price_df['date'].drop_duplicates()\n",
    "        first_indices = price_df.groupby('date').head(1).index\n",
    "\n",
    "        ax.set_xticks(first_indices)\n",
    "        ax.set_xticklabels(unique_dates, rotation=45)\n",
    "\n",
    "        plt.grid(True)\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "023db07d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data from 2024-07-01 to 2024-07-11...\n",
      "Fetching data from 2024-07-11 to 2024-07-21...\n",
      "Fetching data from 2024-07-21 to 2024-07-31...\n",
      "Fetching data from 2024-07-31 to 2024-08-10...\n",
      "Fetching data from 2024-08-10 to 2024-08-20...\n",
      "Fetching data from 2024-08-20 to 2024-08-30...\n",
      "Fetching data from 2024-08-30 to 2024-09-09...\n",
      "Fetching data from 2024-09-09 to 2024-09-19...\n",
      "Fetching data from 2024-09-19 to 2024-09-29...\n",
      "Fetching data from 2024-09-29 to 2024-10-09...\n",
      "Fetching data from 2024-10-09 to 2024-10-19...\n",
      "Fetching data from 2024-10-19 to 2024-10-29...\n",
      "Fetching data from 2024-10-29 to 2024-11-08...\n",
      "Fetching data from 2024-11-08 to 2024-11-18...\n",
      "Fetching data from 2024-11-18 to 2024-11-28...\n",
      "Fetching data from 2024-11-28 to 2024-12-08...\n",
      "Fetching data from 2024-12-08 to 2024-12-18...\n",
      "Fetching data from 2024-12-18 to 2024-12-28...\n",
      "Fetching data from 2024-12-28 to 2025-01-07...\n",
      "Fetching data from 2025-01-07 to 2025-01-17...\n",
      "Fetching data from 2025-01-17 to 2025-01-27...\n",
      "Fetching data from 2025-01-27 to 2025-02-06...\n",
      "Fetching data from 2025-02-06 to 2025-02-16...\n",
      "Fetching data from 2025-02-16 to 2025-02-26...\n",
      "Fetching data from 2025-02-26 to 2025-03-08...\n",
      "Fetching data from 2025-03-08 to 2025-03-18...\n",
      "Fetching data from 2025-03-18 to 2025-03-28...\n",
      "Fetching data from 2025-03-28 to 2025-04-07...\n",
      "Fetching data from 2025-04-07 to 2025-04-17...\n",
      "Fetching data from 2025-04-17 to 2025-04-27...\n",
      "Fetching data from 2025-04-27 to 2025-05-07...\n",
      "Fetching data from 2025-05-07 to 2025-05-17...\n",
      "Fetching data from 2025-05-17 to 2025-05-27...\n",
      "Fetching data from 2025-05-27 to 2025-06-06...\n",
      "Fetching data from 2025-06-06 to 2025-06-16...\n",
      "Fetching data from 2025-06-16 to 2025-06-26...\n",
      "Fetching data from 2025-06-26 to 2025-07-01...\n",
      "Data saved to database.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89d86a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     open      high       low     close  volume             datetime\n",
      "0  588.47  589.1800  588.4200  589.1799  554221  2025-05-21 06:30:00\n",
      "1  589.17  589.5500  589.0100  589.4499  481699  2025-05-21 06:31:00\n",
      "2  589.45  589.6987  589.3500  589.5700  225320  2025-05-21 06:32:00\n",
      "3  589.56  589.6695  589.2793  589.5800  203124  2025-05-21 06:33:00\n",
      "4  589.57  589.6700  589.2700  589.2900  141856  2025-05-21 06:34:00\n"
     ]
    }
   ],
   "source": [
    "conn = sqlite3.connect(\"schwab_data.db\")\n",
    "df = pd.read_sql_query(\"SELECT * FROM price_data LIMIT 100\", conn)  # adjust LIMIT as needed\n",
    "print(df.head())\n",
    "conn.close()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
